STATISTICAL ANXIETY IN UNIVERSITY STUDENTS: THE ROLE OF NEGATIVE PROBLEM ORIENTATION, WORRY, AND THEIR CONSEQUENCES

Authors

DOI:

https://doi.org/10.52041/serj.v24i1.705

Keywords:

Statistics education research, Statistical anxiety, Worry, Negative problem orientation

Abstract

Functional models of anxiety based on dispositional variables have gained scientific acceptance. Their application to investigate constructs such as statistical anxiety can facilitate understanding and intervention. This study aimed to estimate whether dispositional variables such as worry and its negative consequences mediated the relationship between negative problem orientation and statistical anxiety among university students. We evaluated survey responses from a sample of 532 students and tested a multiple mediation model. Negative problem orientation indirectly influenced statistical anxiety through the negative consequences of worry. Negative problem orientation predicted worry but did not mediate the effect on statistical anxiety. Cognitive appraisal of the adverse sequelae of worry is a key variable in the manifestations of statistical anxiety in university students.

References

Abbiati, N. N., Fabrizio, M., López, M. d. C., Pérez, A., Plencovich, M. C., & Cueto, G. (2021). Attitudes related to students’ performance in statistics in university programs in Argentina. Statistics Education Research Journal, 20(2), Article 8. https://doi.org/10.52041/serj.v20i2.356

Ato, M., López, J. J., & Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en psicología [A classification system for research designs in psychology]. Anales de Psicología, 29(3), 1038–1059. https://doi.org/10.6018/analesps.29.3.178511

Baloglu, M. (2003). Individual differences in statistics anxiety among college students. Personality and Individual Differences, 34(5), 855–865. https://doi.org/10.1016/S0191-8869(02)00076-4

Baloglu, M., Abbassi, A., & Kesici, ?. (2017). Multivariate relationships between statistics anxiety and motivational beliefs. Education, 137(4), 430–444.

Barlow, D. H. (2004). Anxiety and its disorders: The nature and treatment of anxiety and panic (2nd ed.). Guilford Press.

Bell, J. (2003). Statistics anxiety: The nontraditional student. Education, 124(1), 157–162.

Beurze, S. M., Donders, A. R., Zielhuis, G. A., Vegt, F. de V., & Verbeek, A. L. (2013). Statistics anxiety: A barrier to education in research methodology for medical students? Medical Science Educator, 23(3), 377–384. https://doi.org/10.1007/BF03341649

Brown, T. A., Antony, M. M., & Barlow, D. H. (1992). Psychometric properties of the Penn State Worry Questionnaire in a clinical anxiety disorders sample. Behaviour Research and Therapy, 30(1), 33–37. https://doi.org/10.1016/0005-7967(92)90093-V

Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270–295. https://doi.org/10.1006/ceps.2001.1094

Chang, E. C, Liu, J., Yi, S., Jiang, X., Li, Q., Wang, R., Tian, W., Gao, X., Li, M., Lucasa, A. G., & Chang, O. D. (2020). Loneliness, social problem solving, and negative affective symptoms: Negative problem orientation as a key mechanism. Personality and Individual Differences, 167, Article 110235. https://doi.org/10.1016/j.paid.2020.110235

Chew, P. K. H., & Dillon, D. B. (2014). Statistics anxiety update. Perspectives on Psychological Science, 9(2), 196–208. https://doi.org/10.1177/1745691613518077

Chiesi, F., & Primi, C. (2010). Cognitive and non-cognitive factors related to students’ statistics achievement. Statistics Education Research Journal, 9(1), 6–26. https://doi.org/10.52041/serj.v9i1.385

Ciarrochi, J., Leeson, P., & Heaven, P. C. L. (2009). A longitudinal study into the interplay between problem orientation and adolescent well-being. Journal of Counseling Psychology, 56(3), 441–449. https://doi.org/10.1037/a0015765

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum Associates.

Colquhoun, D. (2019). The false positive risk: A proposal concerning what to do about p-values. The American Statistician, 73(sup1), 192–201. https://doi.org/10.1080/00031305.2018.1529622

Condron, D. J., Becker, J. H., & Bzhetaj, L. (2018). Sources of students’ anxiety in a multidisciplinary social statistics course. Teaching Sociology, 46(4), 346–355. https://doi.org/10.1177/0092055X18780501

Cox, N. J. (2008). Speaking Stata: Correlation with confidence, or Fisher’s z revisited. The Stata Journal, 8(3), 413–439. https://doi.org/10.1177/1536867X0800800307

Cruise, R., Cash, R., & Bolton, D. (1985). Development and validation of an instrument to measure statistical anxiety. In American Statistical Association 1985 proceedings of the section on statistical education (pp. 92–97). American Statistical Association.

Cui, S., Zhang, J., Guan, D., Zhao, X., & Si, J. (2019). Antecedents of statistics anxiety: An integrated account. Personality and Individual Differences, 144, 79–87. https://doi.org/10.1016/j.paid.2019.02.036

Davey, G. C. L. (1994). Worrying, social problem-solving abilities, and social problem-solving confidence. Behaviour Research and Therapy, 32(3), 327–330. https://doi.org/10.1016/0005-7967(94)90130-9

Davey, G. C. L., Jubb, M. & Cameron, C. (1996). Catastrophic worry as a function of changes in problem-solving confidence. Cognitive Therapy and Research, 20(4), 333–344. https://doi.org/10.1007/BF02228037

Davey, G. C. L., Tallis, F., & Capuzzo, N. (1996). Beliefs about the consequences of worrying. Cognitive Therapy and Research, 20(5), 499–520. https://doi.org/10.1007/bf02227910

Dugas, M. J., & Robichaud, M. (2007). Cognitive-behavioral treatment for generalized anxiety disorder: From science to practice. Routledge/Taylor & Francis Group.

D’Zurilla, T. J, Nezu, A. M., & Maydeu-Olivares, A. (2004). Social problem solving: Theory and assessment. In E. Chang, T. J. D’Zurilla, & L. Sanna (Eds.), Social problem solving: Theory, research, and training (pp. 11–27). American Psychological Association. https://doi.org/10.1037/10805-001

Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 215–217. https://doi.org/10.15406/bbij.2017.05.00149

Faber, G., Drexler, H., Stappert, A., & Eichhorn, J.

(2018). Education science students’ statistics anxiety: Developing and analyzing a scale for measuring their worry, avoidance, and emotionality cognitions. International Journal of Educational Psychology, 7(3), 248–285. https://doi.org/10.17583/ijep.2018.3340

Fergus, T. A., Valentiner, D. P., Wu, K. D., & McGrath, P. B. (2015). Examining the symptom level specificity of negative problem orientation in a clinical sample. Cognitive Behaviour Therapy, 44(2), 153–161. https://doi.org/10.1080/16506073.2014.987314

Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Sage.

Fullerton, J. A., & Umphrey, D. U. (2016). Statistics anxiety and math aversion among advertising students. Journal of Advertising Education, 20(1–2), 135–143. https://doi.org/10.1177/10980482160201-216

Goretzko, D., Siemund, K., & Sterner, P. (2024). Evaluating model fit of measurement models in confirmatory factor analysis. Educational and Psychological Measurement, 84(1), 123–144. https://doi.org/10.1177/00131644231163813

Gosselin, P., Ladouceur, R., & Pelletier, O. (2005). Évaluation de l’attitude d’un individu face aux différents problèmes de vie: le Questionnaire d’Attitude face aux Problèmes [Evaluation of an individual's attitude toward daily life problems: the negative problem orientation questionnaire]. Journal de Thérapie Comportementale et Cognitive, 15(4), 142–153. https://doi.org/10.1016/S1155-1704(05)81235-2

Gosselin, P., Pelletier, O., & Ladouceur, R. (2001, July 17-21). The negative problem orientation questionnaire (NPOQ): Development and validation among a non-clinical sample [Poster presentation]. World Congress of Behavioral and Cognitive Therapies, Vancouver.

Gray, C., & Kinnear, P. (2012). IBM SPSS 19 statistics made simple. Psychology Press.

Greenland, S. (2019). Valid p-values behave exactly as they should: Some misleading criticisms of p-values and their resolution with s-values. The American Statistician, 73(sup1), 106–114. https://doi.org/10.1080/00031305.2018.1529625

Hanna, D., & Dempster, M. (2009). The effect of statistics anxiety on students’ predicted and actual test scores. The Irish Journal of Psychology, 30(3–4), 201–209. https://doi.org/10.1080/03033910.2009.10446310

Hannula, M. (2006). Motivation in mathematics: Goals reflected in emotions. Educational Studies in Mathematics, 63(2), 165–178. https://doi.org/10.1007/s10649-005-9019-8

Hannula, M. (2012). Exploring new dimensions of mathematics related affect: Embodied and social theories. Research in Mathematics Education, 14(2), 137–161. https://doi.org/10.1080/14794802.2012.694281

Hayes, A. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

He, J., Liu, Y., Ran, T., & Zhang, D. (2023). How students’ perception of feedback influences self-regulated learning: The mediating role of self-efficacy and goal orientation. European Journal of Psychology of Education, 38(4), 1551–1569. https://doi.org/10.1007/s10212-022-00654-5

Hedges, S. (2017). Statistics student performance and anxiety: Comparisons in course delivery and student characteristics. Statistics Education Research Journal, 16(1), 320–336. https://doi.org/10.52041/serj.v16i1.233

Hjemdal, O., Stiles, T., & Wells, A. (2012). Automatic thoughts and meta-cognition as predictors of depressive or anxious symptoms: A prospective study of two trajectories. Scandinavian Journal of Psychology, 54(2), 59–65. https://doi.org/10.1111/sjop.12010

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

JASP Team. (2023). JASP (Version 0.17.1) [Computer software]. https://jasp-stats.org/

Jöreskog, K. G. (2003). Factor analysis by MINRES. Scientific Software International.

Kerby, D. (2014). The simple difference formula: An approach to teaching nonparametric correlation. Comprehensive Psychology, 3. https://doi.org/10.2466/11.IT.3.1

Kertz, S., Stevens, K. T., McHugh, R. K., & Björgvinsson, T. (2014). Distress intolerance and worry: The mediating role of cognitive variables. Anxiety, Stress, & Coping, 28(4), 408–424. https://doi.org/10.1080/10615806.2014.974571

Kertz, S., & Woodruff-Borden, J. (2012). The role of metacognition, intolerance of uncertainty, and negative problem orientation in children’s worry. Behavioral and Cognitive Psychotherapy, 41(2), 243–248. https://doi.org/10.1017/s1352465812000641

Kline, R. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Koh, D., & Zawi, M (2014). Statistics anxiety among postgraduate students. International Education Studies, 7(13), 166–174. https://doi.org/10.5539/ies.v7n13p166

Koerner, N., & Dugas, M. J. (2006). A cognitive model of generalized anxiety disorder: The role of intolerance of uncertainty. In G. C. L. Davey & A. Wells (Eds.), Worry and its psychological disorders: Theory, assessment and treatment (pp. 201–216). Wiley Publishing. https://doi.org/10.1002/9780470713143.ch12

Koerner, N., Tallon, K., & Kusec, A. (2015). Maladaptive core beliefs and their relation to generalized anxiety disorder. Cognitive Behaviour Therapy, 44(6), 441–455. https://doi.org/10.1080/16506073.2015.1042989

Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A., & Tomás-Marco, I. (2014). Exploratory item factor analysis: A practical guide revised and updated. Annals of Psychology, 30(3), 1151–1169. https://doi.org/10.6018/analesps.30.3.199361

MacArthur, K. R. (2020). Avoiding over-diagnosis: Exploring the role of gender in changes over time in statistics anxiety and attitudes. Numeracy, 13(1), Article 4. https://doi.org/10.5038/1936-4660.13.1.4

Macher, D., Paechter, M., Papousek, I., & Ruggeri, K. (2012). Statistics anxiety, trait anxiety, learning behavior, and academic performance. European Journal of Psychology of Education, 27(4), 483–498. https://doi.org/10.1007/s10212-011-0090-5

Macher, D., Paechter, M., Papousek, I., Ruggeri, K., Freudenthaler, H. H., & Arendasy, M. (2013). Statistics anxiety, state anxiety during an examination, and academic achievement. British Journal of Educational Psychology, 83(4), 535–549. https://doi.org/10.1111/j.2044-8279.2012.02081.x

Macher, D., Papousek, I., Ruggeri, K., & Paechter, M. (2015). Statistics anxiety and performance: blessings in disguise. Frontiers in Psychology, 6. Article 1116. https://doi.org/10.3389/fpsyg.2015.01116

MacKinnon, D. (2008). Introduction to statistical mediation analysis. Lawrence Erlbaum Associates.

Maneesriwongul, W., & Dixon, J. K. (2004). Instrument translation process: A methods review. Journal of Advanced Nursing, 48(2), 175–186. https://doi.org/10.1111/j.1365-2648.2004.03185.x

Mardia, K. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519-530. https://doi.org/10.1093/biomet/57.3.519

Maydeu-Olivares, A., & D’Zurilla, T. J. (1996). A factor-analytic study of the social problem-solving inventory: An integration of theory and data. Cognitive Therapy and Research, 20(2), 115–133. https://doi.org/10.1007/bf02228030

McIntee, S. E, Goulet-Pelletier, J. C., Williot, A., Deck-Léger, E., Lalande, D., Cantinotti, M., & Cousineau, D. (2022). (Mal)adaptive cognitions as predictors of statistics anxiety. Statistics Education Research Journal, 21(1), Article 5. https://doi.org/10.52041/serj.v21i1.364

McKnight, P. E., & Najab, J. (2010a). Mann-Whitney U test. In I. B. Weiner & W. E. Craighead (Eds.), The Corsini Encyclopedia of Psychology (4th ed., Vol. 1, p. 960). John Wiley & Sons. https://doi.org/10.1002/9780470479216.corpsy0524

McKnight, P. E., & Najab, J. (2010b). Kruskal-Wallis test. In I. B. Weiner & W. E. Craighead (Eds.), The Corsini Encyclopedia of Psychology (4th ed., Vol. 1, p. 904). John Wiley & Sons. https://doi.org/10.1002/9780470479216.corpsy0491

Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State worry questionnaire. Behaviour Research and Therapy, 28(6), 487–495. https://doi.org/10.1016/0005-7967(90)90135-6

Mineka, S. (2004). The positive and negative consequences of worry in the aetiology of generalized anxiety disorder: A learning theory perspective. In J. Yiend (Ed.), Cognition, emotion and psychopathology: Theoretical, empirical and clinical directions (pp. 29–48). Cambridge University Press. https://doi.org/10.1017/CBO9780511521263.003

Morata-Ramírez, M. A, Holgado-Tello, F. P, Barbero-García, M. I., & Méndez, G. (2015). Análisis factorial confirmatorio. Recomendaciones sobre mínimos cuadrados no ponderados en función del error tipo I de ji-cuadrado y RMSEA [Confirmatory factor analysis. Recommendations on unweighted least squares as a function of chi-square type I error and RMSEA]. Acción Psicológica, 12(1), 79–90. https://www.proquest.com/scholarly-journals/análisis-factorial-confirmatorio-recomendaciones/docview/1728282970/se-2

Najmi, A., Raza, S. A, & Qazi, W. (2018). Does statistics anxiety affect students’ performance in higher education? The role of students’ commitment, self-concept, and adaptability. International Journal of Management in Education, 12(2), 95–113. https://doi.org/10.1504/IJMIE.2018.090705

Nezu, A., Maguth, C., & D'Zurilla, T. J. (2010). Problem-Solving Therapy. In Kazantzis, N., Reinecke, M., & Freeman, A. (Eds.). (2010). Cognitive and behavioral theories in clinical practice (pp. 76–114). The Guilford Press.

Observatorio de la Universidad Colombiana. (2020). Presencia de la educación superior en Barranquilla y el Atlántico [Presence of higher education in Barranquilla and the Atlántico region] (Publication 2680). https://www.universidad.edu.co/presencia-de-la-educacion-superior-en-barranquilla-y-el-atlantico/

Oliver, A., Sancho, P., Galiana, L., Cebrià i Iranzo, M. A. (2014). Nueva evidencia sobre la statistical anxiety scale (SAS) [New evidence on the statistical anxiety scale (SAS)]. Anales de Psicología, 30(1), 150–156. https://doi.org/10.6018/analesps.30.1.151341

Onwuegbuzie, A. J., & Wilson. V. A. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments—a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195–209. https://doi.org/10.1080/1356251032000052447

Ouellet, C., Langlois, F., Provencher, M. D., & Gosselin, P. (2019). Intolerance of uncertainty and difficulties in emotion regulation: Proposal for an integrative model of generalized anxiety disorder. Revue Européenne de Psychologie Appliquée, 69(1), 9–18. https://doi.org/10.1016/j.erap.2019.01.001

Paechter, M., Macher, D., Martskvishvili, K., Wimmer, S., & Papousek, I. (2017). Mathematics anxiety and statistics anxiety: Shared but also unshared components and antagonistic contributions to performance in statistics. Frontiers in Psychology, 8, Article 1196. https://doi.org/10.3389/fpsyg.2017.01196

Pan, W., & Tang, M. (2005). Students’ perceptions on factors of statistics anxiety and instructional strategies. Journal of Instructional Psychology, 32(3), 205–214.

Penney, A. M., Mazmanian, D., & Rudanycz, C. (2013). Comparing positive and negative beliefs about worry in predicting generalized anxiety disorder symptoms. Canadian Journal of Behavioural Sciences, 45(1), 34–41. https://doi.org/10.1037/a0027623

Preacher, K., & Hayes, A. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

Putwain, D. W., Sander, P., & Larkin, D. (2013). Academic self-efficacy in study-related skills and behaviours: Relations with learning-related emotions and academic success. British Journal of Educational Psychology, 83(4), 633–650. https://doi.org/10.1111/j.2044-8279.2012.02084.x

Rebeki?, A., Lon?ari?, Z., Petrovi?, S., & Mari?, S. (2015). Pearson’s or Spearman’s correlation coefficient—which one to use? Poljoprivreda, 2, 47–54. https://doi.org/10.18047/poljo.21.2.8

Robichaud, M., & Dugas, M. J. (2004). Negative problem orientation (part I): Psychometric properties of a new measure. Behaviour Research and Therapy, 43(3), 391–401. https://doi.org/10.1016/j.brat.2004.02.007

Robichaud, M., & Dugas, M. J. (2005). Negative problem orientation (part II): Construct validity and specificity to worry. Behaviour Research and Therapy, 43(3), 403–412. https://doi.org/10.1016/j.brat.2004.02.008

Rodarte-Luna, B., & Sherry, A. (2008). Sex differences in the relation between statistics anxiety and cognitive learning strategies. Contemporary Educational Psychology, 33(2), 327–344. https://doi.org/10.1016/j.cedpsych.2007.03.002

Ryum, T., Kennair, L. E, Hjemdal, O., Hagen, R., Halvorsen, J. Ø., & Solem, S. (2017). Worry and metacognitions as predictors of anxiety symptoms: A prospective study. Frontiers in Psychology, 8, Article 924. https://doi.org/10.3389/fpsyg.2017.00924

Scotta, A. V., Cortez, M. V., & Miranda, A. R. (2020). Insomnia is associated with worry, cognitive avoidance and low academic engagement in Argentinian university students during the COVID-19 social isolation. Psychology, Health & Medicine, 27(1), 119–214. https://doi.org/10.1080/13548506.2020.1869796

Schuwirth, L. (2012). “Emotions in learning” is more than merely “learning of emotions.” Medical Education, 47(1), 14–15. https://doi.org/10.1111/medu.12078

Sesé, A., Jiménez, R., Montaño, J. J., Palmer, A. (2015). Can attitudes toward statistics and statistics anxiety explain students’ performance? Revista de Psicodidáctica, 20(2), 285–304. https://doi.org/10.1387/RevPsicodidact.13080

Sica, C., Steketee, G., Ghisi, M., Chiri, L. R., & Franceschini, S. (2007). Metacognitive beliefs and strategies predict worry, obsessive-compulsive symptoms and coping styles: A preliminary prospective study on an Italian non-clinical sample. Clinical Psychology & Psychotherapy, 14(4), 258–268. https://doi.org/10.1002/cpp.520

Stensen, K. & Lydersen, S. (2022). Internal consistency: From alpha to omega? Tidsskr Nor Legeforen, 142(12). https://doi.org/10.4045/tidsskr.22.0112

Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16, 209–220. https://doi.org/10.1037/a0023353

Valentine, J. C., Aloe, A. M., & Lau, T. S. (2015). Life after NHST: How to describe your data without “p-ing” everywhere. Basic and Applied Social Psychology, 37(5), 260–273. https://doi.org/10.1080/01973533.2015.1060240

Van Gundy, G. K., Morton, B. A., Liu, H. Q., & Kline, J. (2006). Effects of web-based instruction on math anxiety, the sense of mastery, and global self-esteem: A quasi-experimental study of undergraduate statistics students. Teaching Sociology, 34(4), 370–388. https://doi.org/10.1177/0092055X0603400404

Vigil-Colet, A., Lorenzo-Seva, U., & Condon, L. (2008). Development and validation of the statistical anxiety scale. Psicothema, 20(1), 174–180.

von der Embse, N., Jester, D., Roy, D., & Post, J. (2017). Test anxiety effects, predictors, and correlates: A 30-year meta-analytic review. Journal of Affective Disorders, 227, 483–493. https://doi.org/10.1016/j.jad.2017.11.048

Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “p < 0.05.” The American Statistician, 73(sup1), 1–19. https://doi.org/10.1080/00031305.2019.1583913

Wells, A. (1995). Meta-cognition and worry: A cognitive model of generalized anxiety disorder. Behavioral and Cognitive Psychotherapy, 23(3), 301–320. https://doi.org/10.1017/S1352465800015897

Wells, A. (1997). Cognitive therapy of anxiety disorders: A practice manual and conceptual guide. Wiley.

Wells, A. (2005). The metacognitive model of GAD: Assessment of meta-worry and relationship with DSM-IV generalized anxiety disorder. Cognitive Therapy and Research, 29(1), 107–121. https://doi.org/10.1007/s10608-005-1652-0

Wells, A. (2010). Metacognitive theory and therapy for worry and generalized anxiety disorder: Review and status. Journal of Experimental Psychopathology, 1(1). 133–145. https://doi.org/10.5127/jep.007910

Wells, A., & Cartwright-Hatton, S. (2004). A short form of the metacognitions questionnaire: Properties of the MCQ-30. Behaviour Research and Therapy, 42(4), 385–396. https://doi.org/10.1016/S0005-7967(03)00147-5

West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). The Guilford Press.

Williams, A. S. (2013). Worry, intolerance of uncertainty, and statistics anxiety. Statistics Education Research Journal, 12(1), 48–59. https://doi.org/10.52041/serj.v12i1.321

Williams, A. S. (2015). Statistics anxiety and worry: The roles of worry beliefs, negative problem orientation, and cognitive avoidance. Statistics Education Research Journal, 14(2), 53–75. https://doi.org/10.52041/serj.v14i2.261

Yilmaz, A. E., Gencöz, T., & Wells, A. (2011). The temporal precedence of metacognition in the development of anxiety and depression symptoms in the context of life-stress: A prospective study. Journal of Anxiety Disorder, 25(3), 389–396. https://doi.org/10.1016/j.janxdis.2010.11.001

Downloads

Published

2025-03-17

Issue

Section

Regular Articles